{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "from scipy import stats\n",
    "import pymysql\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n",
    "plt.rcParams['font.family'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>851104</td>\n",
       "      <td>2017-01-21 22:11:48.556739</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>804228</td>\n",
       "      <td>2017-01-12 08:01:45.159739</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>661590</td>\n",
       "      <td>2017-01-11 16:55:06.154213</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-01-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>853541</td>\n",
       "      <td>2017-01-08 18:28:03.143765</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>864975</td>\n",
       "      <td>2017-01-21 01:52:26.210827</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>936923</td>\n",
       "      <td>2017-01-10 15:20:49.083499</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-01-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>679687</td>\n",
       "      <td>2017-01-19 03:26:46.940749</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-01-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>719014</td>\n",
       "      <td>2017-01-17 01:48:29.539573</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-01-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>817355</td>\n",
       "      <td>2017-01-04 17:58:08.979471</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>839785</td>\n",
       "      <td>2017-01-15 18:11:06.610965</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-01-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  user_id                   timestamp      group landing_page converted  \\\n",
       "0  851104  2017-01-21 22:11:48.556739    control     old_page         0   \n",
       "1  804228  2017-01-12 08:01:45.159739    control     old_page         0   \n",
       "2  661590  2017-01-11 16:55:06.154213  treatment     new_page         0   \n",
       "3  853541  2017-01-08 18:28:03.143765  treatment     new_page         0   \n",
       "4  864975  2017-01-21 01:52:26.210827    control     old_page         1   \n",
       "5  936923  2017-01-10 15:20:49.083499    control     old_page         0   \n",
       "6  679687  2017-01-19 03:26:46.940749  treatment     new_page         1   \n",
       "7  719014  2017-01-17 01:48:29.539573    control     old_page         0   \n",
       "8  817355  2017-01-04 17:58:08.979471  treatment     new_page         1   \n",
       "9  839785  2017-01-15 18:11:06.610965  treatment     new_page         1   \n",
       "\n",
       "         date  \n",
       "0  2017-01-21  \n",
       "1  2017-01-12  \n",
       "2  2017-01-11  \n",
       "3  2017-01-08  \n",
       "4  2017-01-21  \n",
       "5  2017-01-10  \n",
       "6  2017-01-19  \n",
       "7  2017-01-17  \n",
       "8  2017-01-04  \n",
       "9  2017-01-15  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con = pymysql.connect(host = 'localhost',\n",
    "                      port = 3306,\n",
    "                      user = 'root',\n",
    "                      password = '696926ouziming',\n",
    "                      db = 'abtest',\n",
    "                      use_unicode = True,\n",
    "                      charset = 'utf8')\n",
    "data = pd.read_sql('select * from ab_data',con = con)\n",
    "data['date'] = data.timestamp.str[:10]\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 检验指标的确认"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一类指标：用户留存率下降"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "二类指标：用户点击率上升"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 确定检验统计量"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一类指标：用户留存率 = Banner结束时首日用户留存数/Banner首日点击用户数，用两组的留存率之差来作为统计量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "二类指标：用户点击率 = Banner点击/Banner曝光，用两组的用户点击率之差来作为统计量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 埋点数据收集"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 确定H0、H1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一类指标：H0：对照组用户留存率 – 实验组用户留存率 >= 2*实验前留存率标准差"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "          H1：对照组用户留存率 – 实验组用户留存率 < 2*实验前留存率标准差"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "二类指标：H0：实验组广告点击率 – 对照组广告点击率 <= 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "          H1：实验组广告点击率 – 对照组广告点击率 > 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 确定显著性水平"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一类错误显著性水平：α = 0.05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "二类错误显著性水平：β = 0.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 计算样本量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于一类指标没有数据，我们只计算二类指标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据二类指标的H0和H1来看，统计量属于比例之差单侧（右侧）检验，所以选用的计算公式如下："
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1203863045004612"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "α = 0.05\n",
    "β = 0.2\n",
    "# 计算对照组比例P值,通过计算其均值就能得出其比例值\n",
    "control_p = (data.converted[(data.group == 'control')&(data.landing_page == 'old_page')&(data.converted == '1')].count())\\\n",
    "/(data.converted[(data.group == 'control')&(data.landing_page == 'old_page')].count())\n",
    "control_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10589344218918344"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算pB(1-pB)\n",
    "control_p_1_control_p = control_p*(1-control_p)\n",
    "control_p_1_control_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6448536269514722"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算 Z 1-α\n",
    "# ppf 可以作为均值之差的右侧检验\n",
    "Z1_α = stats.norm.ppf(1-α)\n",
    "Z1_α"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8416212335729143"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算 Z 1-β\n",
    "Z1_β = stats.norm.ppf(1-β)\n",
    "Z1_β"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.014999999999999986"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 为了让样本量最大，treatment_p - control_p的值要尽量小\n",
    "# 设定 treatment_p - control_p > 0.015\n",
    "treatment_p = control_p + 0.015\n",
    "pA_pB = treatment_p - control_p\n",
    "pA_pB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11705685305416959"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算pA(1-pA)\n",
    "treatment_p_1_treatment_p = treatment_p*(1-treatment_p)\n",
    "treatment_p_1_treatment_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6126.235378834387"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算最小样本量\n",
    "n = (treatment_p_1_treatment_p + control_p_1_control_p) * ((Z1_α + Z1_β)/pA_pB)**2\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>date</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>6590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>6578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>6427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>6606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-07</td>\n",
       "      <td>6604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-08</td>\n",
       "      <td>6687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-09</td>\n",
       "      <td>6628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-10</td>\n",
       "      <td>6654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-11</td>\n",
       "      <td>6688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-12</td>\n",
       "      <td>6522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-13</td>\n",
       "      <td>6552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-14</td>\n",
       "      <td>6548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-15</td>\n",
       "      <td>6714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-16</td>\n",
       "      <td>6591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-17</td>\n",
       "      <td>6617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-18</td>\n",
       "      <td>6482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-19</td>\n",
       "      <td>6578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-20</td>\n",
       "      <td>6534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-21</td>\n",
       "      <td>6749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-22</td>\n",
       "      <td>6596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-23</td>\n",
       "      <td>6716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>2017-01-24</td>\n",
       "      <td>3754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>6618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>6541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>6505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>6747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-07</td>\n",
       "      <td>6609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-08</td>\n",
       "      <td>6700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-09</td>\n",
       "      <td>6615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-10</td>\n",
       "      <td>6696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-11</td>\n",
       "      <td>6673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-12</td>\n",
       "      <td>6637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-13</td>\n",
       "      <td>6508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-14</td>\n",
       "      <td>6600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-15</td>\n",
       "      <td>6549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-16</td>\n",
       "      <td>6545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-17</td>\n",
       "      <td>6538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-18</td>\n",
       "      <td>6603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-19</td>\n",
       "      <td>6552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-20</td>\n",
       "      <td>6679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-21</td>\n",
       "      <td>6560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-22</td>\n",
       "      <td>6669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-23</td>\n",
       "      <td>6633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>2017-01-24</td>\n",
       "      <td>3681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        group landing_page        date  user_id\n",
       "23    control     old_page  2017-01-02     2859\n",
       "24    control     old_page  2017-01-03     6590\n",
       "25    control     old_page  2017-01-04     6578\n",
       "26    control     old_page  2017-01-05     6427\n",
       "27    control     old_page  2017-01-06     6606\n",
       "28    control     old_page  2017-01-07     6604\n",
       "29    control     old_page  2017-01-08     6687\n",
       "30    control     old_page  2017-01-09     6628\n",
       "31    control     old_page  2017-01-10     6654\n",
       "32    control     old_page  2017-01-11     6688\n",
       "33    control     old_page  2017-01-12     6522\n",
       "34    control     old_page  2017-01-13     6552\n",
       "35    control     old_page  2017-01-14     6548\n",
       "36    control     old_page  2017-01-15     6714\n",
       "37    control     old_page  2017-01-16     6591\n",
       "38    control     old_page  2017-01-17     6617\n",
       "39    control     old_page  2017-01-18     6482\n",
       "40    control     old_page  2017-01-19     6578\n",
       "41    control     old_page  2017-01-20     6534\n",
       "42    control     old_page  2017-01-21     6749\n",
       "43    control     old_page  2017-01-22     6596\n",
       "44    control     old_page  2017-01-23     6716\n",
       "45    control     old_page  2017-01-24     3754\n",
       "46  treatment     new_page  2017-01-02     2853\n",
       "47  treatment     new_page  2017-01-03     6618\n",
       "48  treatment     new_page  2017-01-04     6541\n",
       "49  treatment     new_page  2017-01-05     6505\n",
       "50  treatment     new_page  2017-01-06     6747\n",
       "51  treatment     new_page  2017-01-07     6609\n",
       "52  treatment     new_page  2017-01-08     6700\n",
       "53  treatment     new_page  2017-01-09     6615\n",
       "54  treatment     new_page  2017-01-10     6696\n",
       "55  treatment     new_page  2017-01-11     6673\n",
       "56  treatment     new_page  2017-01-12     6637\n",
       "57  treatment     new_page  2017-01-13     6508\n",
       "58  treatment     new_page  2017-01-14     6600\n",
       "59  treatment     new_page  2017-01-15     6549\n",
       "60  treatment     new_page  2017-01-16     6545\n",
       "61  treatment     new_page  2017-01-17     6538\n",
       "62  treatment     new_page  2017-01-18     6603\n",
       "63  treatment     new_page  2017-01-19     6552\n",
       "64  treatment     new_page  2017-01-20     6679\n",
       "65  treatment     new_page  2017-01-21     6560\n",
       "66  treatment     new_page  2017-01-22     6669\n",
       "67  treatment     new_page  2017-01-23     6633\n",
       "68  treatment     new_page  2017-01-24     3681"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看原始数据中的样本量是否满足\n",
    "data_group = data.groupby(['group','landing_page','date'],as_index=False)['user_id'].count()\n",
    "data_group[((data_group.group == 'control') & (data_group.landing_page == 'old_page') \\\n",
    "| (data_group.group == 'treatment') & (data_group.landing_page == 'new_page'))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  利用统计工具实现检验"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据两个总体的比例之差公式，我们要算出对照组和实验组的点击率的均值"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>control</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0.205128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0.115350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0.123462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>treatment</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0.154639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       group landing_page  converted\n",
       "0    control     new_page   0.205128\n",
       "1    control     old_page   0.115350\n",
       "2  treatment     new_page   0.123462\n",
       "3  treatment     old_page   0.154639"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['converted'] = data['converted'].astype('int64')\n",
    "data_convert_mean = data[data.date == '2017-01-06'].groupby(by = ['group','landing_page'],as_index = False)['converted'].mean()\n",
    "data_convert_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.008112597424468404"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 算点击率的均值之差\n",
    "mean_diff = data_convert_mean.converted[2] - data_convert_mean.converted[1]\n",
    "mean_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6606 6747\n"
     ]
    }
   ],
   "source": [
    "# 统计对照组和实验组的个数\n",
    "n1 = data[(data.date == '2017-01-06') \\\n",
    "     & (data.group == 'control') \\\n",
    "     & (data.landing_page == 'old_page')].converted.count()\n",
    "\n",
    "n2 = data[(data.date == '2017-01-06') \\\n",
    "     & (data.group == 'treatment') \\\n",
    "     & (data.landing_page == 'new_page')].converted.count()\n",
    "print(n1,n2)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.007936472059632065"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 算标准差\n",
    "sigma = np.sqrt(data_convert_mean.converted[2]*(1 - data_convert_mean.converted[2])/n1) \\\n",
    "+ np.sqrt(data_convert_mean.converted[1]*(1 - data_convert_mean.converted[1])/n2)\n",
    "sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15334504274244737"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算比例之差的P值\n",
    "# 此处因为没有在一开始明确指标需要提升的量，所以这里的提升量为0\n",
    "mean_diff_P = stats.norm.sf(mean_diff,0,sigma)\n",
    "mean_diff_P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "实验组广告点击率 – 对照组广告点击率 <= 0\n"
     ]
    }
   ],
   "source": [
    "if mean_diff_P >= α:\n",
    "    print('实验组广告点击率 – 对照组广告点击率 <= 0')\n",
    "else :\n",
    "    print('实验组广告点击率 – 对照组广告点击率 > 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         group landing_page  converted\n",
       "29   treatment     new_page          0\n",
       "104  treatment     new_page          0\n",
       "120    control     old_page          0\n",
       "126  treatment     new_page          1\n",
       "139  treatment     new_page          0"
      ]
     },
     "execution_count": 512,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test = data.loc[(data.date == '2017-01-06'),('group','landing_page','converted')]\n",
    "data_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ABTest封装函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {},
   "outputs": [],
   "source": [
    "def abtest(df: pd.DataFrame, alpha=0.05, group_col: str = None, value_col: str = None):\n",
    "    '''\n",
    "    :param df: 被分析DateFrame对象\n",
    "    :param alpha: 临界值\n",
    "    :param group_col: 组列的名字，默认为df的第一列\n",
    "    :param value_col: 值列的名字,默认为df的第2列\n",
    "    :return:best_group_name,pdf\n",
    "        best_group_name:最优组\n",
    "        pdf:最优组与其他组的差异性\n",
    "    '''\n",
    "    # 列名\n",
    "    if not group_col:\n",
    "        group_col0 = df.columns[0]\n",
    "        group_col1 = df.columns[1]\n",
    "    if not value_col:\n",
    "        value_col2 = df.columns[2]\n",
    "\n",
    "    sample_mean = df.groupby([group_col0,group_col1],as_index = False)[value_col2].mean()\n",
    "    sample_num = df.groupby([group_col0,group_col1],as_index = False)[value_col2].count()\n",
    "    sample_mean_diff = sample_mean.iloc[2,2] - sample_mean.iloc[1,2]\n",
    "    sample_std = np.sqrt(sample_mean.iloc[2,2]*(1 - sample_mean.iloc[2,2])/ sample_num.iloc[2,2])\\\n",
    "    + np.sqrt(sample_mean.iloc[1,2]*(1 - sample_mean.iloc[1,2])/ sample_num.iloc[1,2])\n",
    "    \n",
    "      \n",
    "    sample_mean_diff_rightP =1-stats.norm.cdf(sample_mean_diff,loc=0,scale=sample_std) \n",
    "    sample_mean_diff_leftP = stats.norm.cdf(sample_mean_diff,loc=0,scale=sample_std)\n",
    "    sample_mean_diff_doubleP = sample_mean_diff_leftP * 2\n",
    "        \n",
    "    if sample_mean_diff_rightP >= alpha:\n",
    "        ptype = \"实验组广告点击率 – 对照组广告点击率 <= 0\"\n",
    "    else:\n",
    "        ptype = \"实验组广告点击率 – 对照组广告点击率 > 0\"\n",
    "        # 添加数据\n",
    "    table = pd.DataFrame = {'实验组/对照组':'点击率之差',\n",
    "                         'mean_diff':sample_mean_diff,\n",
    "                         'pvalue':sample_mean_diff_rightP,\n",
    "                         'ptype':ptype}\n",
    "\n",
    "    return table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'实验组/对照组': '点击率之差',\n",
       " 'mean_diff': 0.008112597424468404,\n",
       " 'pvalue': 0.15330753222326643,\n",
       " 'ptype': '实验组广告点击率 – 对照组广告点击率 <= 0'}"
      ]
     },
     "execution_count": 540,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abtest(data_test)"
   ]
  }
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